In addition to the usual convolutional parameters of a CNN, our emulator contains extra parameters at each layer that encode the \(\Omega _\mathrm {m}\)  dependence of clustering across multiple scales. The value of \(\Omega _\mathrm {m}\)  is fed to each layer of the CNN and transformed to an array with the same dimension as the layer's input. The convolution kernels are multiplied by this array prior to performing the convolution. We described this in greater detail in the materials and methods section. Following StyleGAN2 [1]}, we refer to \(\Omega _\mathrm {m}\)  as a style parameter, and we refer to the emulator as the styled neural network (SNN) model. We compare this model against two competitors. The first has the same neural network architecture except it does not include the style parameter, so it has no cosmology dependence. We refer to this model as the fiducial neural network (FNN) model, since it was trained on a set of N-body simulations in the Quijote simulation suite with only the fiducial values of the five cosmological parameters. This comparison allows us to evaluate the gains in accuracy achieved by including the style parameter, which gives the SNN its cosmology dependence.
